Exploring fractal dimensions on ultrasound of gonadal images for sex determination in shortnose sturgeon ( <i>Acipenser brevirostrum</i> )
Bibliographic record
Abstract
Abstract Sturgeon are often viewed as monomorphic species because they often lack external features that allow differentiation between sexes. Current practices for sexing sturgeon rely heavily on surgical invasive procedures to visually examine gonadal tissues. An alternative which is widely used in different species of Acipenserids is the use of ultrasound to sex them. The challenge with ultrasound is that it requires an experienced operator to successfully sex the fish by recognizing relevant patterns in the structures of gonads. The objective of this study is to attempt to lay the groundwork for potential systematization of sexing techniques using a portable ultrasound as a medium. We used texture analysis software based on lacunarity measurements and fractal dimensions to determine whether male gonads, female granular tissue (GT) and female pinheads were significantly different from each other using two different resolutions on the ultrasound machine. Male gonads and GT were significantly different from pinheads in lacunarity measurements (p=0.001) using 10-12Mhz general resolution imaging. Fractal dimensions did not result in any significant differences. Lacunarity has the potential to determine sex based on gray level-co-occurrence matrices and the software is freely available. We have also developed a relative probability table based on the data gathered on lacunarity in this study available as supplementary material for quick reference.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".